Identifying causal genetic variants and molecular mechanisms impacting mental health

识别影响心理健康的因果遗传变异和分子机制

• 批准号: 10571911
• 负责人: Anshul Kundaje
• 金额: $ 61.6万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10571911
• 关键词: ATAC-seq; Acute; Affect; Alleles; Alzheimer' s Disease; Biological Assay; Biology; Brain; Brain region; Catalogs; Cells; Chromatin; Collaborations; Communities; Computer software; Computing Methodologies; DNA Sequence; Data; Databases; Detection; Development; Disease; Future; Gene Expression; Gene Expression Regulation; Gene Frequency; Genes; Genetic; Genetic Diseases; Genetic Load; Genetic Risk; Genetic Variation; Genotype-Tissue Expression Project; Human; Individual; Influentials; Learning; Link; Linkage Disequilibrium; Machine Learning; Maps; Masks; Mental Health; Mental Health Associations; Mental disorders; Methods; Modeling; Molecular; Molecular Disease; Mus; National Human Genome Research Institute; Neurodevelopmental Disorder; Organoids; Outcome; Parkinson Disease; Pathologic; Performance; Population; Quantitative Trait Loci; Reporter; Reproducibility; Research; Resolution; Resources; Risk; Tissue Sample; Tissues; Training; Uncertainty; Untranslated RNA; Variant; Weight; Work; base; biobank; brain cell; causal variant; cell type; computational pipelines; deep learning model; disorder risk; epigenome; epigenomics; experimental study; functional genomics; genetic variant; genome resource; genome wide association study; genomic locus; improved; insight; machine learning method; machine learning model; novel; novel strategies; open data; polygenic risk score; prevent; public health relevance; regression algorithm; risk prediction; success; therapy development; trait

Identifying how genetic variation leads to neurodevelopmental or psychiatric disorders provides new means to study, predict, prevent and treat disease. Identifying the immediate molecular consequences of disease- associated genetic variation has necessitated the development of large-scale, multi-tissue functional genomic resources. Projects such as GTEx, Roadmap Epigenomics Project and PsychENCODE have combined molecular QTL mapping and epigenomic maps in bulk tissues to interpret various disease-associated genetic variants. However, few colocalizations between molecular QTLs and traits have been robustly identified and few causal variants mapped. As tissues like the brain constitute 100s of cell-types, we hypothesize that existing maps may mask the contributions of disease-associated variation in less-abundant cell types. One extremely powerful approach to identify cell-type specific molecular effects and their relationship to genetic diseases is through application of chromatin accessibility data – these data both allow inference of causal cell types and provide base level resolution gene regulation. Our team has considerable expertise in connecting GWAS to molecular functions and predicting causal variants through use of chromatin accessibility data. We have additionally recently collaborated to generate a comprehensive, multi-individual map single cell ATAC- seq map (scATAC-seq) of six different brain regions to detect causal cell types and predict causal variants. This work has been recently demonstrated in our fine-mapping study of Alzheimer’s and Parkinson’s disease (Corces et al, bioRxiv, 2020) but has not been systematically applied to mental health disorders. We propose to develop statistical genetics and machine learning approaches that advance the use of scATAC-seq data to connecting mental health GWAS loci to specific cell types, mechanisms and causal variants. In Aim 1, we will assemble a pipeline that leverages region and cell type-specific scATAC-seq data to identify pathological cell types for 100s of mental health and brain-related traits. We will also enhance the detection of cell-type specific molecular mechanisms by extending and applying a novel GWAS/QTL colocalization approach. Throughout these activities, variants will be validated using massively-parallel reporter assays (MPRA). In Aim 2, we will develop sophisticated machine learning models that learn regulatory grammars and score variants across the allele frequency spectrum. Predicted causal variants in GWAS loci will be further assessed with MPRAs in Aim 1 and applied in Aim 3. In Aim 3, we will demonstrate how improved detection of causal variants using our single-cell informed models aids transferability of polygenic risk scores across populations. We will provide open resources and reproducible computational methods and pipelines that integrate single cell chromatin accessibility data from multiple brain regions. This will allow detection cell-type specific genetic effects and pathological cell types in mental health GWAS, establish robust causal links between variants, genes and disease, and improve prediction of disease risk.

确定遗传变异如何导致神经发育或精神疾病提供了新的手段， 研究、预测、预防和治疗疾病。识别疾病的直接分子后果- 相关的遗传变异使得开发大规模、多组织的功能基因组 资源GTEx、Roadmap表观基因组学项目和PsychENCODE等项目结合了 分子QTL定位和表观基因组图谱，以解释各种疾病相关的遗传 变体。然而，分子QTL和性状之间的共定位很少被稳健地鉴定， 很少有因果变异映射。由于像大脑这样的组织由100种细胞类型组成，我们假设， 现有的图谱可能掩盖了不太丰富的细胞类型中疾病相关变异的贡献。一 一种非常强大的方法来识别细胞类型特异性分子效应及其与遗传的关系， 疾病是通过应用染色质可及性数据-这些数据都允许因果细胞的推断 类型，并提供基本水平的分辨率基因调控。我们的团队在连接 GWAS的分子功能和预测因果变异通过使用染色质可及性数据。我们 此外，最近还合作生成了一个全面的，多个人的地图单细胞ATAC- 我们使用六个不同大脑区域的seq图（scATAC-seq）来检测因果细胞类型并预测因果变体。 这项工作最近在我们对阿尔茨海默氏症和帕金森氏症的精细绘图研究中得到了证实 （Corces等人，bioRxiv，2020），但尚未系统地应用于精神健康疾病。我们提出 开发统计遗传学和机器学习方法，促进scATAC-seq数据的使用， 将心理健康GWAS基因座与特定的细胞类型、机制和因果变异联系起来。在目标1中，我们 组装利用区域和细胞类型特异性scATAC-seq数据来识别病理细胞的流水线 100多个心理健康和大脑相关特征的类型。我们还将加强检测细胞类型特异性 通过扩展和应用新的GWAS/QTL共定位方法来研究其分子机制。在整个 这些活性，变体将使用重复平行报告基因测定（MPRA）进行验证。在目标2中，我们将 开发复杂的机器学习模型，学习监管语法，并在整个 等位基因频率谱将在Aim中使用MPRA进一步评估GWAS基因座中预测的因果变异 1并应用于目标3。在目标3中，我们将演示如何使用我们的 单细胞知情模型有助于多基因风险评分在人群中的可转移性。 我们将提供开放的资源和可复制的计算方法和管道， 来自多个大脑区域的单细胞染色质可及性数据。这将允许检测细胞类型特异性 精神健康GWAS中的遗传效应和病理细胞类型，在 变异，基因和疾病，并改善疾病风险的预测。